Gonzalo UribarriBiträdande lektor
Om mig
I am an Associate Senior Lecturer at the Department of Computer and Systems Sciences, Stockholm University, since September 2025. Originally from Buenos Aires, Argentina, I received my PhD in Physics from the University of Buenos Aires in 2022 and subsequently held a postdoctoral position at KTH Royal Institute of Technology from 2022 to 2025.
My research centers on machine learning and time series modeling with applications to biomedical and neuroscientific data. In addition to my academic work, I co-founded Palmarium AI, a fintech company focused on AI-based solutions for quantitative finance.
Undervisning
- [2023] Teaching Assistant — KTH, Royal Institute of Technology. Course DD2437: Artificial Neural Networks and Deep Architectures
- [2021, 2023] Project Teaching Assistant & Mentor— Neuromatch Academy: Deep Learning Course.
- [2013 – 2020] Teaching Assistant — Faculty Of Exact And Natural Sciences, University Of Buenos Aires. Courses: Dynamical Systems and Artificial Intelligence, Nonlinear Dynamics, Complex Networks and Physics for Biologists
Forskning
I work at the intersection of machine learning, time series modeling, and biomedical data analysis. My research mainly focuses on neuroscientific questions and healthcare applications, involving data modalities such as EEG, MEG and eye tracking. In addition to biomedicine, I have experience in financial data modelling and system identification reflecting my broader interest in using data-driven methods to model and understand complex systems.
Publications:
- af Edholm, K., Uribarri, G., Sundgren, M., Svenningsson, A., & Fransén, E. (2025). Orthostatic Tremor Is Evoked by Muscle Load Without the Need for Orthostatic Position. Movement Disorders Clinical Practice.
- Solana, A., Fransén, E., & Uribarri, G. (2024). Classification of raw MEG/EEG data with detach-rocket ensemble: an improved rocket algorithm for multivariate time series analysis. Proceedings of AALTD at ECML-PKDD conference.
- Uribarri, G., Barone, F., Ansuini, A., & Fransén, E. (2024). Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels. Data Mining and Knowledge Discovery, 1–26.
- Uribarri, G., von Huth, S. E., Waldthaler, J., Svenningsson, P., & Fransén, E. (2023). Deep Learning for Time Series Classification of Parkinson’s Disease Eye Tracking Data. ML4H Findings Track Collection, Machine Learning for Health (ML4H) Conference (2023).
- Uribarri, G., & Mindlin, G. B. (2022). Dynamical time series embeddings in recurrent neural networks. Chaos, Solitons & Fractals, 154, 111612.
- Uribarri, G., & Mindlin, G. B. (2020). The structure of reconstructed flows in latent spaces. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(9).
- Uribarri, G., Rodríguez-Cajarville, M. J., Tubaro, P. L., Goller, F., & Mindlin, G. B. (2020). Unusual avian vocal mechanism facilitates encoding of body size. Physical Review Letters, 124(9), 098101.
- Boari, S., Uribarri, G., Amador, A., & Mindlin, G. B. (2019). Observable for a Large System of Globally Coupled Excitable Units. Mathematical and Computational Applications, 24(2), 37.
- Uribarri, G., & Mindlin, G. B. (2019). Resonant features in a forced population of excitatory neurons. arXiv Preprint arXiv:1902. 06008.
Selected Presentations in Conferences:
- ECML PKDD 2024 Conference: Workshop on Advanced Analytics and Learning on Temporal Data
- Contributed Talk - 2024 September 9-13, Vilnius, Lithuania.
- DDLS Annual Conference 2023: The Emerging Role Of Ai In Data-Driven Life Science
- Contributed Talk - 2023 November 15-16, Stockholm, Sweden.
- Yachay’s Scientific Computing Summer School 2021
- Invited Speaker - 2021 September, Ciudad Del Conocimiento Yachay, Ecuador.
- Statphys 27 - International Conference On Statistical Physics
- Poster Presentation - 22019 July 8-12, Buenos Aires, Argentina.
- Annual Meeting Of The International Physics Of Living Systems (Ipols) Network
- Poster Presentation - 2017 July 25-29, Institut Pierre Gilles De Gennes, Paris, France.
Organization of Events
- BRAINNET+ 2024. Main Organizer, 13-15 May, 2024, Stockholm, Sweden. Link
- Interpretable Brain Data 2023. Main Organizer, 8-9 June, 2023, Stockholm, Sweden. Link
Grants & Funds Awarded
- Vinnova: Advanced and innovative digitization 2024 (Swedish Innovation Agency). Project: "Personalized protective ventilation of intensive care patients using a digital twin."
- KTH Life Science Platform. Collaboration and networking within Life Science. Funding received in 2022 & 2023.
- CONICET PhD Scholarship (Argentinian Research Agency). Received from 2016 to 2021.
Master Thesis Supervision
- Adrià Solana (defended in February 2024)
- Shou Zheyun (defended in June 2025)
Scientifics Referee
- 12 reviewed articles for Elsevier journals.
Publikationer
Recent Publications:
_ af Edholm, K., Uribarri, G., Sundgren, M., Svenningsson, A., & Fransén, E. (2025). Orthostatic Tremor Is Evoked by Muscle Load Without the Need for Orthostatic Position. Movement Disorders Clinical Practice.
_ Solana, A., Fransén, E., & Uribarri, G. (2024). Classification of raw MEG/EEG data with detach-rocket ensemble: an improved rocket algorithm for multivariate time series analysis. Proceedings of AALTD at ECML-PKDD conference.
_ Uribarri, G., Barone, F., Ansuini, A., & Fransén, E. (2024). Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels. Data Mining and Knowledge Discovery, 1–26.
_ Uribarri, G., von Huth, S. E., Waldthaler, J., Svenningsson, P., & Fransén, E. (2023). Deep Learning for Time Series Classification of Parkinson’s Disease Eye Tracking Data. ML4H Findings Track Collection, Machine Learning for Health (ML4H) Conference (2023).